During the past decade, deep learning (DL) has led to significant breakthroughs in many areas, such as image classification and natural language processing [1]}, [2]}, [3]}. However, the existing large models and computation complexity limit the deployment of DL on resource-constrained devices and its large-scale adoption in edge computing. Multiple model compression techniques, such as network pruning [4]}, quantization [5]}, and knowledge distillation [6]}, have been proposed to compress and deploy such complex models on resource-constrained devices without sacrificing the test accuracy.
However, these techniques require a significant amount of manual tuning. Hence, neural architecture search (NAS) has been proposed to automatically design neural architectures with reduced model sizes [7]}, [8]}, [9]}, [10]}, [11]}.
